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This paper presents a novel design of a prosthetic foot that features adaptable stiffness that changes according to the speed of ankle motion. The motivation is the natural graduation in stiffness of a biological ankle over a range of ambulation tasks. The device stiffness depends on rate of movement, ranging from a dissipating support at very slow walking speed, to efficient energy storage and return at normal walking speed. The objective here is to design a prosthetic foot that provides a compliant support for slow ambulation, without sacrificing the spring-like energy return beneficial in normal walking. The design is a modification of a commercially available foot and employs material properties to provide a change in stiffness. The velocity dependent properties of a non-Newtonian working fluid provide the rate adaptability. Material properties of components allow for a geometry shift that results in a coupling action, affecting the stiffness of the overall system. The function of an adaptive coupling was tested in linear motion. A prototype prosthetic foot was built, and the speed dependent stiffness measured mechanically. Furthermore, the prototype was tested by a user and body kinematics measured in gait analysis for varying walking speed, comparing the prototype to the original foot model (non-modified). Mechanical evaluation of stiffness shows increase in stiffness of about 60% over the test range and 10% increase between slow and normal walking speed in user testing.

Electric Capacitance, Electrodes, Electrophoresis, Equipment Design, Microfluidic Analytical Techniques instrumentation, and Microfluidics methods

In this paper, we present a new rapid prototyping platform dedicated to dielectrophoretic microfluidic manipulation and capacitive cell sensing. The proposed platform offers a reconfigurable design including 4 independently programmable output channels to be distributed across 64 electrodes. Although its range of frequency covers up to 3.4 MHz, signal amplitude accuracy ( +/-10%) was demonstrated for frequencies up to 1 MHz and channel-to-channel phase shift setting was stable up to 1.5 MHz. A test of maximum resistive load showed a 10% attenuation of a 12 V peak-to-peak signal with a 22 Ω load. The platform has an advanced capacitive sensor to measure capacitance variation between in-channel electrodes with a sampling frequency up to 5 kH z. Experimental data of capacitive sensor showed a sensitivity of 100 fF. The sensor can be extended to 4 parallel measurements with lower frequency. We also present a new assembly technique for reusable microfluidic chip based on anisotropic adhesive conductive film, epoxy and PDMS. The proposed platform provides a wide range of control signals depending on the type of manipulation as sine, rectangular or square wave. The frequency range is extendible up to 3.4 MHz, in addition to a programmable phase shift circuit with a minimum phase step of 3.6(°) for each signal.

Adolescent, Adult, Algorithms, Communication Aids for Disabled, Computer Systems, Cortical Synchronization instrumentation, Discriminant Analysis, Equipment Design instrumentation, Humans, Least-Squares Analysis, Male, Regression Analysis, Reproducibility of Results, Time Factors, Brain physiopathology, Electroencephalography instrumentation, Neuromuscular Diseases physiopathology, and User-Computer Interface

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

This paper presents an ultrasonically powered microsystem for deep tissue optogenetic stimulation. All the phases in developing the prototype starting from modelling the piezoelectric crystal used for energy harvesting, design, simulation and measurement of the chip, and finally testing the whole system in a mimicking setup are explained. The developed system is composed of a piezoelectric harvesting cube, a rectifier chip, and a micro-scale custom-designed light-emitting-diode (LED), and envisioned to be used for freely moving animal studies. The proposed rectifier chip with a silicon area of [Formula: see text] is implemented in standard TSMC [Formula: see text] CMOS technology, for interfacing the piezoelectric cube and the microLED. Experimental results show that the proposed microsystem produces an available electrical power of  [Formula: see text] while loaded by a microLED, out of an acoustic intensity of [Formula: see text] using a [Formula: see text] crystal as the receiver. The whole system including the tested rectifier chip, a piezoelectric cube with the dimensions of [Formula: see text], and a μLED of [Formula: see text] have been integrated on a [Formula: see text] glass substrate, encapsulated inside a bio-compatible PDMS layer and tested successfully for final prototyping. The total volume of the fully-packaged device is estimated around [Formula: see text].

Humans, Algorithms, Electromyography, Gestures, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, and Wearable Electronic Devices

This paper presents a wearable electromyographic gesture recognition system based on the hyperdimensional computing paradigm, running on a programmable parallel ultra-low-power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the state-of-the-art, with the unique capability of performing online learning. Furthermore, by virtue of the hardware friendly algorithm and of the efficient PULP system-on-chip (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04 mJ, and 83.2  μJ per classification. The system works with a average power consumption of 10.4 mW in classification, ensuring around 29 h of autonomy with a 100 mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.

Adult, Artifacts, Artificial Limbs, Biomechanical Phenomena, Calibration, Electromyography, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Motion, Prosthesis Design, Tibia diagnostic imaging, Amputation, Extremities diagnostic imaging, Image Processing, Computer-Assisted methods, and Ultrasonography methods

Ultrasound is a cost-effective, readily available, and non-ionizing modality for musculoskeletal imaging. Though some research groups have pursued methods that involve submerging the transducer and imaged body segment into a water bath, many limitations remain in regards to acquiring an unloaded volumetric image of an entire human limb in a fast, safe, and adequately accurate manner. A 3D dataset of a limb is useful in several rehabilitative applications including biomechanical modeling of soft tissue, prosthetic socket design, monitoring muscle condition and disease progression, bone health, and orthopedic surgery. This paper builds on previous work from our group and presents the design, prototyping, and preliminary testing of a novel multi-modal imaging system for rapidly acquiring volumetric ultrasound imagery of human limbs, with a particular focus on residual limbs for improved prosthesis design. Our system employs a mechanized water tank setup to scan a limb with a clinical ultrasound transducer and 3D optical imagery to track motion during a scan. The iterative closest point algorithm is utilized to compensate for motion and stitch the images into a final dataset. The results show preliminary 2D and 3D imaging of both a tissue-mimicking phantom and residual limbs. A volumetric error compares the ultrasound image data obtained to a previous MRI method. The results indicate potential for future clinical implementation. Concepts presented in this paper could reasonably transfer to other imaging applications such as acoustic tomography, where motion artifact may distort image reconstruction.

Planar optofluidics provide a powerful tool for facilitating chip-scale light-matter interactions. Silicon-based liquid core waveguides have been shown to offer single molecule sensitivity for efficient detection of bioparticles. Recently, a PDMS based planar optofluidic platform was introduced that opens the way to rapid development and prototyping of unique structures, taking advantage of the positive attributes of silicon dioxide-based optofluidics and PDMS based microfluidics. Here, hydrodynamic focusing is integrated into a PDMS based optofluidic chip to enhance the detection of single H1N1 viruses on-chip. Chip-plane focusing is provided by a system of microfluidic channels to force the particles towards a region of high optical collection efficiency. Focusing is demonstrated and enhanced detection is quantified using fluorescent polystyrene beads where the coefficient of variation is found to decrease by a factor of 4 with the addition of hydrodynamic focusing. The mean signal amplitude of fluorescently tagged single H1N1 viruses is found to increase with the addition of focusing by a factor of 1.64.

Adult, Biomechanical Phenomena, Computer Simulation, Equipment Design, Female, Healthy Volunteers, Humans, Male, Models, Theoretical, Reproducibility of Results, Spinal Cord Injuries rehabilitation, Walking physiology, Walking Speed, Young Adult, Exoskeleton Device, and Lower Extremity

Lower extremity powered exoskeletons (LEPEs) allow people with spinal cord injury (SCI) to stand and walk. However, the majority of LEPEs walk slowly and users can become fatigued from overuse of forearm crutches, suggesting LEPE design can be enhanced. Virtual prototyping is a cost-effective way of improving design; therefore, this research developed and validated two models that simulate walking with the Bionik Laboratories' ARKE exoskeleton attached to a human musculoskeletal model. The first model was driven by kinematic data from 30 able-bodied participants walking at realistic slow walking speeds (0.2-0.8 m/s) and accurately predicted ground reaction forces (GRF) for all speeds. The second model added upper limb crutches and was driven by 3-D-marker data from five SCI participants walking with ARKE. Vertical GRF had the strongest correlations (>0.90) and root-mean-square error (RMSE) and mediolateral center of pressure trajectory had the weakest (<0.35), for both models. Strong correlations and small RMSE between predicted and measured GRFs support the use of these models for optimizing LEPE joint mechanics and improving LEPE design.

3D printing technology has been widely used as a rapid prototyping fabrication tool in several fields, including electrochemistry. In this work, we incorporate 3D printing technology with carbon nanotube yarns for electrochemical sensing of dopamine in the presence of ascorbic acid and uric acid. The novel 3D printed electrode provides a circular concavity detection zone with grooves to insert three electrodes. The electrode connections are fully compatible with conventional screen printed electrode workstation setups. The CNT yarn 3D printed electrode showed excellent electrocatalytic activity for the redox reaction of dopamine (DA) in the presence of ascorbic acid (AA) and uric acid (UA). Three well-defined sharp and fully resolved anodic peaks were found with the peak potentials using cyclic voltammetry (CV) at 50 mV, 305 mV, and 545 mV for AA, DA, and UA respectively and using differential pulse voltammetry (DPV) at 91 mV, 389 mV, and 569 mV, respectively. DA detection limit was 0.87 ± 0.09 μM. The CNT yarn 3D printed electrode displayed high reproducibility and stability. The electrode design enables the study of electrode reactions at the sidewall of CNTs, which cannot be performed using electrodes made by conventional fabrication methods. The new fabrication method provides a new platform to prototype new electrode materials for electrochemistry, providing a low-cost, customizable design compatible existing screen printed electrodes technology.

Bone Cements, Equipment Design, Femur surgery, Humans, Models, Biological, Arthroplasty, Replacement, Hip instrumentation, Electric Impedance, Signal Processing, Computer-Assisted instrumentation, and Surgical Instruments

A bioimpedance-controlled concept for bone cement milling during revision total hip replacement is presented. Normally, the surgeon manually removes bone cement using a hammer and chisel. However, this procedure is relatively rough and unintended harm may occur to tissue at any time. The proposed bioimpedance-controlled surgical instrumentation improves this process because, for example, most risks associated with bone cement removal are avoided. The electrical bioimpedance measurements enable online process-control by using the milling head as both a cutting tool and measurement electrode at the same time. Furthermore, a novel integrated surgical milling tool is introduced, which allows acquisition of electrical bioimpedance data for online control; these data are used as a process variable. Process identification is based on finite element method simulation and on experimental studies with a rapid control prototyping system. The control loop design includes the identified process model, the characterization of noise as being normally distributed and the filtering, which is necessary for sufficient accuracy ( ±0.5 mm). Also, in a comparative study, noise suppression is investigated in silico with a moving average filter and a Kalman filter. Finally, performance analysis shows that the bioimpedance-controlled surgical instrumentation may also performs effectively at a higher feed rate (e.g., 5 mm/s).